OpenAI's math discoveries: Are we teaching students to think big enough?

When AI enters the world of mathematical discovery

OpenAI's latest mathematical discoveries offer a glimpse into a future where intellectual achievement is increasingly shared between humans and machines.

In October 2026, OpenAI announced that its frontier AI model had produced new mathematical results, including contributions to previously unsolved problems. While mathematicians continue to examine and verify the findings, the announcement points towards exciting possibilities for human intellectual discovery.

Mathematics has long represented one of the highest expressions of human intelligence, combining reasoning, abstraction, creativity and discovery. The possibility that AI can contribute at this level invites us to reconsider longstanding assumptions about intellectual capability.

For universities, it raises a fascinating question: If AI can help us discover things we could not discover alone, what should we be teaching the next generation?

I believe two capabilities deserve particular attention: learning to work with other forms of intelligence, and developing the curiosity and imagination to explore possibilities beyond our individual capabilities.

Learning to work with other forms of intelligence

As AI becomes capable of contributing to increasingly sophisticated intellectual work, the ability to collaborate with different forms of intelligence could become a fundamental part of being educated.

Human intelligence has always developed alongside tools, technologies and other people. We think with language, books, instruments, computers and one another. AI introduces new possibilities into these relationships.

Imagine a student exploring a complex scientific problem with an AI system capable of generating hypotheses, testing approaches and identifying connections beyond the student's existing knowledge.

The student's contribution could involve deciding which problems are worth investigating, recognising promising directions, challenging the machine's reasoning and making sense of the results.

These are substantial intellectual activities, requiring knowledge, judgement and the ability to work with capabilities different from our own.

Yet access to advanced AI does not automatically develop these abilities. Students may learn to collaborate intelligently with AI, or become increasingly dependent on outputs they cannot evaluate.

Universities have an important role in helping students develop their own intellectual foundations while learning when and how to draw on machine intelligence.

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Expanding intellectual curiosity and imagination

Perhaps the most exciting educational opportunity AI offers is the expansion of what people can imagine, explore and achieve.

Throughout history, what people have been able to imagine pursuing has been shaped by what they believed was possible. Resources, expertise, time and access to knowledge have constrained which questions could realistically be explored, and by whom. Is AI changing that dynamic? I think so.

A student might investigate a question that previously required an entire research team. Someone with expertise in one discipline might explore connections with another that would otherwise have taken years to develop. Ideas once too expensive or complicated to test could become accessible to many more people. And this expansion of possibility could have profound implications for education.

Curiosity, imagination and the confidence to venture beyond established boundaries may become increasingly important. Students will need the intellectual courage to ask ambitious questions, the capability to explore them and the judgement to understand what their discoveries mean.

I would like universities to create environments where students experience this expanded possibility firsthand, developing the confidence to imagine and pursue futures beyond what they initially thought achievable.

What might universities become?

As the boundaries of intellectual possibility expand, universities have an opportunity to reconsider their educational ambitions and the capabilities they help students develop.

Universities have traditionally organised education around disciplinary knowledge and progressively demonstrated individual mastery. These remain essential foundations, particularly when students need sufficient understanding to critically evaluate sophisticated AI-generated work.

Yet our educational ambitions have also been shaped by assumptions about what individual learners can reasonably achieve at different stages of their development. I think AI might be pushing us to revisit those assumptions.

What if undergraduate education gave students more opportunities to participate in meaningful discovery from the beginning? What if learning were increasingly organised around ambitious problems, with disciplinary knowledge, human expertise and AI supporting students as they explored them?

Realising these possibilities will require experimentation with curricula, pedagogy and assessment, alongside careful attention to intellectual development and the risks of dependency.

At the London School of Innovation, this thinking informs our exploration of AI-native education: how emerging technologies can expand the possibilities for learning and help people develop the capabilities to shape their own futures.

OpenAI's mathematical announcement illustrates how the boundaries of intellectual possibility are moving. Universities have agency in deciding how those expanded possibilities influence the education they provide.

Perhaps we have been too constrained by what students can achieve independently. As intelligence becomes something we can increasingly collaborate with beyond ourselves, we have an opportunity to raise our ambitions for what human curiosity, imagination and capability can achieve.

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